The morbidity of brain tumors is relatively high, and especially for malignant lesions that children are susceptible to, brain tumor is second only to leukemia. Brain tumors, whether in a benign or malignant form, will elevate intracranial pressure, constrict brain tissues, cause damage in central nervous system, and endanger a patients' life. The location and quantization calculation (such as calculating the volume, the diameter, etc. of a tumor) for lesional tissues of brain tumors are extremely important for diagnosing brain tumors, making treatment plans, and monitoring curative effects. In clinical practice, radiologists usually segment tumors manually with a multimodal magnetic resonance image, which is a considerably tedious and time consuming job. However, with the technology of computerized automatic segmentation of brain tumors, doctors may be liberated from this job, and mistakes resulting from fatigue may be effectively avoided as well. Therefore, the technology of automatic segmentation of brain tumors has a special significance in adjuvant therapy for brain tumors. Wherein, the most common type of brain tumors is gliomas, and presently most algorithms for automatic segmentation of brain tumors are specific to gliomas. The segmentation of gliomas based on magnetic resonance images is a considerably challenging job. The difficulty thereof is embodied in the following aspects: (1) In a magnetic resonance image, gliomas and other diseases, such as gliosis, stroke, etc., may have similar appearance; (2) Gliomas may occur at any location of the brain in any shape or size, thus, little transcendental knowledge may be utilized in the process of segmentation; (3) Gliomas usually invade surrounding tissues rather than substitute them, which makes the edge of the tumor blurred in the magnetic resonance image; (4) Magnetic resonance imaging devices are imperfect, so it is inevitable that a certain extent of non-uniform brightness field will be present in a magnetic resonance image, which also raises the difficulty of brain tumor segmentation. Although it is extraordinarily hard to achieve precise automatic segmentation of brain tumors, due to the enormous significance of it in adjuvant therapy, in the past decades, numerous researchers have been attracted to devote themselves to researching it. At present, prior methods for brain tumor segmentation may be mainly divided into two types: one is based on generative models, and the other is based on discriminative models. The generative models depend on transcendent knowledge in the professional field. One of the frequently used methods for acquiring transcendent knowledge is to use brain atlas. This method registers a brain atlas onto a target image, on the basis of the image matching criterion of maximizing information, so as to obtain probability graphs of white matter, gray matter, and cerebrospinal fluid in the target image, and subsequently segmenting brain tumors using methods such as active contour, etc., in accordance with the probability graphs and other features such as texture, brightness, etc. Gooya et al. obtain a more accurate probability graph by using a tumor growth model, and further improve the precision of tumor segmentation. However, if the tumor is relatively large or the brain has been treated with resection procedure, the whole brain structure will undergo a deformation, at this point, the probability graph obtained from matching is usually not reliable.
The discriminative models generally implement tumor segmentation by extracting features of voxels, such as local histogram, texture, etc., and then classifying the voxels in accordance with the features. Classifiers, such as SVMs, random forests, etc., have all been used for brain tumor segmentation. The precision of segmentation of a discriminative model depends on the quality of manually designed features, whereas up till now, not a single feature has been found that is not only simple but also enable to provide sufficient discrimination between healthy tissues and lesional tissues.
At present, deep learning has already been successfully applied to multiple fields including automatic segmentation of brain tumors. Havaei et al. segment tumors using a convolutional neural network which has two branches and a serial structure. Pereira et al. use a technology of a plurality of convolutional layers with small convolution kernels instead of convolutional layers with large convolution kernels in a neural network for tumor segmentation and achieve prominent effect. However, presently, the technology of brain tumor segmentation based on deep learning has great difficulty in ensuring the continuity of the segmentation result in shape and in space. In order to solve this problem, it is necessary to combine deep learning with probability graph models.